In order to accelerate delivery and reduce network congestion, Internet Service Providers (“ISPs”) often deploy cache servers at strategic locations in the network for storing frequently requested content. These cache servers are physically located closer to end users and therefore are able to deliver content to those end users much faster than the content servers themselves, which could be physically located at the fringes of the network. Furthermore, these cache servers reduce network congestion because by storing frequently requested content, they minimize the need to contact the actual content servers, thereby reducing “upstream” traffic. For example, America Online, Inc. (“AOL”) currently utilizes Inktomi's® Traffic Server, a network caching platform, to speed up Web access for its users.1 When a user requests a Web page, the request is routed to the closest cache server in the AOL network. If the requested Web page is located in the cache server and is current, then the cache server delivers the Web page directly to the user without the need to access the Web server. If the Web page is not located in the cache, the cache server acts as a proxy and fetches the Web page from the Web server on the user's behalf. 1 See <http://www.inktomi.com/products/network/products/tscclass.html>.
Such cache server schemes are most effective for dealing with static content that does not change or only changes slowly over time. However, since cache servers are only effective as long as their content is current, they have difficulty handling dynamically generated content that quickly changes over time. It is therefore inefficient to cache dynamic content since, by definition, such content will change upon subsequent retrievals. As such, innovative methods for accelerating the delivery of dynamically generated content have been developed. For example, FineGround Networks, Inc. has developed Condensation™ technology that “condenses” dynamic content in real-time. A FineGround “condenser,” typically disposed between the cache (or other content server) and the network users, condenses Web pages by eliminating redundancies between successive downloads of the same page.2 The FineGround Condensation™ technology uses a process hereafter referred to as “delta-encoding” in which a base version of a dynamic document is both locally stored (e.g., in a browser or network cache) and remotely stored at the condenser. When the condenser receives a new request for the dynamic document from the client, the condenser transmits to the user a representation of the difference (in the form of a “condensed document”) between the current version and the base version of the dynamic document. Transmission of the condensed document, rather than a complete document, requires less network bandwidth, thereby freeing up bandwidth for other transmissions and accelerating the delivery to the user. Upon receipt of the condensed document, the client uses the locally stored base version to construct the current version. This delta-encoding process is the subject matter of co-pending application Ser. No. 09/634,134, filed Aug. 8, 2000, which is hereby incorporated by reference. 2 See <http://www.fineground.com/prod/whitepaper.html>.
It is often the case that dynamic documents are template-based or otherwise share the same context and/or characteristics. As such, documents that possess similar layouts can be classified so that content in one document within a “class” can be condensed against content in a different document within the same class. Thus, regardless of which user generates requests, all requests for dynamic documents that belong to the same class can be serviced by the same class base file. This class base file is stored both locally at the client and also remotely at the condenser so that delta-encoding techniques can be utilized.
Nevertheless, current methods for configuring and managing class-based condensation can be improved. First, techniques to efficiently identify and create classes should be developed. Currently, an administrator manually identifies classes that possess similar layouts such that content within that class can be condensed against content within the same class. Furthermore, techniques for selecting an efficient class base file should also be developed. It is beneficial to select an efficient class base file because the performance of the delta-encoding will depend upon how similar the class base file is to the requested dynamic documents (within that class). Currently, however, class base files are typically manually created when a new class is identified by the administrator. As such, the class base file is typically the first requested dynamic document that is a member of the class. It is clear that such an arbitrary document may not necessarily be the best document to serve as the class base file. Therefore, there remains a great need for an automated mechanism to efficiently configure and manage classed-based condensation.